Observatories
1. Introduction
Conservation planning and management requires ready access to relevant sources of data and knowledge. Observatories are multi-partner and multi-actor organizations that gather, curate, and share data and knowledge about the changing state of ecological components of a geographic region. Observatories combine observation technologies spanning to produce standardized variables and indicators of change that can guide investments in conservation action and resource management. Guidelines for designing and implementing observatories that support the monitoring of biodiversity and ecosystem processes and services are available (Navarro et al. 2017).
Several national and international organizations support the design and implementation of observation networks in northern regions. These include:
The Group on Earth Observations Biodiversity Observation Network (GEO BON)
The Sustaining Arctic Observing Networks (SAON)
The Pan-Arctic Observing System of Systems (Arctic PASSION)
The Research Network Activities for Sustained Coordinated Observations of Arctic Change (RNA CoObs) that supports SAON.
The Canadian Consortium for Arctic Data Interoperability (CCADI)
Observatories often include community-based monitoring initiatives working with local communities and Indigenous Peoples to address specific conservation questions and knowledge needs. For example, the Indigenous Community-Based Climate Monitoring Program in Canada supports Indigenous-led projects that monitor climate and the environmental effects of climate change within community boundaries and on traditional territories using Traditional Knowledge Systems and Western Science.
Community-based observing programs provide the opportunity to weave multiple knowledge systems, monitor biodiversity, environment and human well-being; and report back to local communities to inform decision making and support their needs and priorities (Hauser et al. 2023). Evidence suggests that participatory monitoring drives knowledge in protected areas worldwide (Mandeville et al. 2023). Key initiatives are active in the HJBL and represent components of an observatory such as the Cree Geoportal (https://www.creegeoportal.ca/).
In the following section we describe the structure and role of a biodiversity observation network and how it can combine a range of knowledge systems and monitoring methods and traditions.
2. Biodiversity Observation Networks (BON)
A BON is a network of observation sites or stations and a network of groups who produce and use biodiversity data across these sites for different needs. A BON coordinates observations and monitoring to support policy and environmental legislation prompting conservation action from national biodiversity strategies and action plans. A BON is designed to overcome the major challenges associated with monitoring the changing state of biodiversity with the temporal and spatial resolution and scope required to supply the information and knowledge to support policy action.
Guidelines for network establishment are publicly available by GEO BON (Navarro et al. 2017). They describe how to create an ‘enabling environment’ that assembles the partnerships, human capacity and scientific infrastructure needed to build a BON. A BON can be national, subnational or regional in its level of operation and can cover different biomes (for example, marine or freshwater) and dimensions of biodiversity (such as genetics, species and ecosystems) to fill specific knowledge gaps. These needs have been recognized by the formation of marine, freshwater, soil and omic BONs.
National BONs can act as national units and networks bringing biodiversity change data and information together. Sub-national BONs are strategic initiatives that can address specific needs and realities of multiple partners and respond to particular ecological conditions within the country (e.g., Kühl et al. 2020). Some multinational networks exist that represent collaborations among national BONs. These include the Asia Pacific BON, the European network (EuropaBON), and the Arctic BON.
The Parks Canada initiative in the Hudson-James Bay region may wish to deploy a BON to support the gathering of data and knowledge by both western and indigenous science.
2.1. What does an operational BON do
A BON starts with the formation of an actor-partnership network, this involves partnerships between organizations that gather and curate biodiversity knowledge with those that use data and braid traditional knowledge and western scientific systems to understand where and why biodiversity is changing. Collaboration among these groups establishes an observation network that produces and braids biodiversity knowledge in a holistic understanding of environmental change. Traditional Knowledge is formed by continued interaction and observation of species and ecosystems and is transferred over many generations. As an organisation, a BON is therefore particularly suited to assessing short and long-term environmental changes and their consequences for people.
Over the last 15 years GEOBON has provided a series of recommendations for realizing a monitoring system that recognizes the value of different knowledge sources and types to track progress, understand change and guide action (Navarro et al. 2017, Gonzalez et al. 2023). The framework includes multiple steps, and we present the key elements of each step.
The framework applies locally to biodiversity and ecosystem services, helping to understand how they are changing. For example, the framework can be used to guide data collection efforts on fish populations to produce indicators relevant to fisheries that support the management of populations and allow Canada to report on the state of its wild fish populations.
The activities of the BON structured, depicted below, according to several modules interconnected by flows of data and information that produce new knowledge about the changing state of biodiversity.
Data collection, curation and sharing of existing knowledge on biodiversity: Existing knowledge on biodiversity change is mobilized and shared, while additional data is collected and continually shared, building a deep knowledge, and understanding of biodiversity and its processes.
Organizations can utilize existing sources of biodiversity knowledge developed by governments, non-government agencies, research institutes, indigenous peoples and local communities, and civil societies. For example, community-based monitoring and information systems (CBMIS) focused on local level efforts and complemented by systematic data collection on species population and ecosystem monitoring schemes (Moussy et al. 2021) can be coordinated to produce a rich understanding of biodiversity change. Local traditional or Indigenous knowledge can not only add to observations, but guide the monitoring itself, for example by helping to determine the relevant spatial and ecological units that are to be observed, and to set targets that are also meaningful locally. The agreed-upon set of observations can be translated into metrics and indicators where needed to harmonize observations across schemes, which supports the accumulation and sharing of biodiversity data and knowledge across systems and space. he development of national and sub-national BONs can be supported by the use of BON in a Box, a platform that combines tools for the entire process of designing, planning, monitoring and evaluating the state and trends in biodiversity and ecosystem services.
Bottom-up and top-down approaches for data collection should be linked. This ensures that locally and nationally collected data support the estimation and use of indicators relevant to supporting decisions in the region (Burgass et al. 2021, Eicken et al. 2021, Nicholson et al. 2021). A bottom-up and top-down approach promotes the data from field-based observations made by different groups (e.g., professional biologists, local indigenous communities and citizens) using a range of technologies for in-situ data acquisition and sharing, with data from a top-down approach involving Earth observation and remote sensing (e.g. space agencies satellites derived products, Ferrier 2011, Kühl et al. 2020, Skidmore et al. 2021). This two-way flow of information on the state biodiversity is necessary to support decisions at local, regional, national, and global scales. GEO BON and its partners are developing all components of this knowledge system, including interoperable tools to facilitate national-scale monitoring and indicator reporting systems.
For such an integrated system to be effective, gaps in the taxonomic and geographic coverage of data should continue to be filled through prioritized data collection. Standardized data sets in public databases should be utilized (e.g. Ocean Biodiversity Information System, OBIS1; Global Biodiversity Information Facility, GBIF) and shared following FAIR - findable, accessible, interoperable and reusable - (Wilkinson et al. 2016) and CARE - collective benefit, authority to control, responsibility and ethics - (Carroll et al. 2020) principles.
Monitoring science: Common standards and protocols for best practices in monitoring and modelling are followed and links established between scientists and non-professional data collectors to improve the flow of information from data to indicator and policy through well-coordinatedinvestment in monitoring and ongoing data collection.
Regional and national BONs can harmonize the collection of data and tacit knowledge on biodiversity trends and drivers. BONs, as partnerships of data providers and users, support the coordination and harmonization of data collection across biodiversity and other environmental observation systems (e.g. for climate, or other physical variables) for large regions. For interoperability, much of the collected biodiversity data is processed into essential variables (e.g. essential biodiversity variables, EBVs; essential ecosystem services EESVs; essential ocean variables, EOVs, and essential climate variables, ECVs) and used to produce indicators, employing common, but flexible indicator methodologies that are applied at multiple (national to local) scales (delivered via common, but customizable tools and toolkits).
Biodiversity information should be integrated for scale-appropriate use. Fine scale and local data sets can inform local policy and may be aggregated for regional assessments. Global monitoring data sets may be disaggregated (top-down) where relevant to inform local and nationally relevant actions and fill gaps.
Planning and prioritization: Knowledge is used to produce indicators that enableattribution of observed biodiversity change to drivers (direct and indirect). This informs strategic planning ofactions to manage biodiversity, prioritise actions and adapt monitoring programs to improve data collection and understanding of biodiversity change.)
Three complementary considerations required the use of indicators with effective monitoring. The first is to track overall progress made towards targets through designed monitoring schemes that cover relevant geographic gradients and events over time while balancing logistical constraints (e.g. human resources and technologies). The second is to progressively improve knowledge of how drivers cause biodiversity change, thereby allowing observed changes in trends to be attributed to changes in drivers and actions. The third approach uses leading indicators to inform strategic planning (including prioritization) of actions to achieve targets and goals effectively and efficiently (Stevenson et al. 2021). This includes forecasting of biodiversity and ecological function and scenario planning to evaluate possible outcomes of management actions. For this, we use leading indicators (currently not included in the GBF monitoring framework) which use best-available understanding of these dependencies – at the time a given decision is made – to predict the expected impact of the proposed or implemented actions on biodiversity outcomes.
All three of these approaches are important and play complementary roles in an overall adaptive policy and planning framework guided by biodiversity monitoring. Indicators to comprehensively cover outcomes, drivers and key interdependencies between them are essential to set priorities and guide action. BONs enable the production of the observations needed to support indicator use over time. Depending on these results, additional or different monitoring efforts may be called for and adaptive monitoring frameworks. BONs the flexibility to respond to these needs.
Reporting progress: The indicators estimated from monitoring data support the production of national reports that describe progress made towards achieving the targets and goals of the GBF.
The outcome of actions and adaptive monitoring efforts contribute to the global effort to protect biodiversity and ecosystem services. Understanding national and global progress towards the GBF follows a defined protocol set out by the UN Convention on Biological Diversity and relies on its monitoring framework. The indicators agreed for inclusion in the monitoring framework of the GBF are included in the national biodiversity strategy and action plans (NBSAPs) and national reports (recommendationSBSTTA 24/2), and in other planning and policy instruments at national and subnational levels. Actions can be adjusted according to trends in predictive (leading) indicators. Compliance and accountability mechanisms, including the use of component, complementary and other national indicators, are in place to review the implementation of NBSAPs and their relevance to GBF goals and targets (Xu et al. 2021).
National monitoring is mainstreamed within national statistical systems for environmental accounts (i.e., UN SEEA) and across key sectors that impact biodiversity (e.g., agriculture, mining, etc.). Financial support and opportunities should be in place, and can be updated, to support and incentivize the implementation of the GBF and monitoring framework.
Vukamanovic et al. (2023) addressed “the practice of translating narratives into models and using those narratives to interpret and communicate results. Involving stakeholders’ perspectives through narratives in participatory modeling fosters better understanding of the problem and evaluation of the acceptability of tradeoffs and creates buy-in for management actions. However, stakeholder-driven inputs often take the form of complex qualitative descriptions, rather than model-ready numerical or categorical inputs”. They offer eight starting points lessons to translate narrative into models:
- Understand what stakeholders want from the process
- Find the right team for the job
- Define boundary conditions as a team
- Find the right tool(s)
- Evaluate model trade-off with stakeholders
- Use an iterative process to adapt, refine and pivot
- Focus on the process as much as the outcome
- Use a synthesis narrative to bring together multiple perspectives
2.2. Monitoring with Essential Variables (EVs)
Essential variables are standardized variables critical for detecting changes in the structure and dynamics of a facet of Earth’s system (e.g., climate, ocean, biodiversity). All essential variables represent a minimum collection of data or measurements that capture a specific dimension of the phenomena of interest and are critical for detecting change across space and time.
Essential variables are therefore located at the interface between data (primary observations), indicators (synthetic or derived metrics), and user-specific societal or policy goals (e.g., Sustainable Development Goals, Balvanera et al.2022, see also Kim et al. 2023). Integrating in-situ observations, remote sensing (e.g., Saranya et al. 2024, Skidmore et al. 2021), modelling, and other tools is fundamental to support the production of essential variables and their use in monitoring (Fernández et al. 2020).
Over the last decade, GEOBON has developed two main groups of essential variables to support monitoring, and modeling of biodiversity and ecosystem services change: Essential Biodiversity Variables (EBVs; Pereira et al. 2013) and Essential Ecosystem Services Variables (EESVs; Balvanera et al. 2022).
The UN CBD has endorsed the EBV framework as means to support monitoring of biodiversity and ecosystem services and for reporting progress under the monitoring framework of the GBF (UN CBD, 2022). EBVs are biological state variables that capture different dimensions of biodiversity change within a specified range of spatial and temporal grain and extent (Schmeller et al. 2017, Bellingham et al. 2020). There are six EBV classes—genetic composition, species populations, species traits, community composition, ecosystem functioning and ecosystem structure—that group twenty-one EBVs.
EBVs are also scalable, meaning the underlying observations can be used to represent required for the analysis of trends at different spatial or temporal resolutions. For example, ecological community data collected at a location from different sampling events or methods can be combined into a single multivariate analysis for the analysis of dissimilarity or turnover in community composition (Ferrier et al. 2007). The aggregated data may indicate the change in ecological communities across the region resulting from land use change or climate change.
The process of operationalizing EBVs is shown in the Figure below. The EBV data “cube” has dimensions of space, time and EBV. The cube is filled by biodiversity observations made by people, depositing raw observations into databases using standard formats and metadata (e.g. Humboldt extension to the Darwin core; Guralnick et al. 2017), and processing the data. The information in the EBV cube helps to detect and model biodiversity change for science and policy (lower box). The underlying drivers and pressures of biodiversity change can then be identified (Gonzalez et al. 2023) and modelled (Oliver et al. 2015). Validation of modelling can then feed into global and regional policy processes to explain observations, to improve forecasting of biodiversity change, and to support regional and global assessment reports.
A subset of EBVs can be monitored from space to support the analysis of geospatial patterns over large spatial extents (Skidmore et al. 2021). A comprehensive list of remote sensing biodiversity products is available that can support the monitoring of biodiversity patterns via Earth observation. The ecosystem structure and ecosystem function EBV classes, which can also capture the ecological effects of disturbance, as well as habitat structure, were shown by an expert review process to be the most relevant, feasible, accurate and mature classes for direct monitoring of biodiversity via satellites. Other EBVs, including movement (Jetz et al. 2022) and community composition are increasingly monitored by remote sensing (Cavender-Bares et al. 2022). Open access to workflows translating remote sensing products into EBVs will accelerate the reporting of the state of biodiversity from local to global scales.
Action to ensure the long-term sustainability of ecosystems and livelihoods of people living in the HBJL will require systematic monitoring of ecosystem services (ES) (IPBES 2019). However, ecosystem service studies typically rely on one-off assessments and often lack methodological standardization, hindering comparisons across space and time. To ensure strong evidence to support trend detection, many policy initiatives now require systematic information on ES change (e.g., Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services – IPBES, System of Environmental-Economic Accounting Ecosystem Accounting – SEEA-EA, UN CBD GBF). This context requires ES monitoring to be standardized, policy-relevant, economically feasible, and interoperable across contexts, levels, and services.
To standardize the monitoring of ecosystem services, GEO BON proposed the Essential Ecosystem Service Variables framework (EESVs; Balvanera et al. 2022). EESVs track interactions between people and nature in social-ecological systems, particularly how nature contributes to human well-being through ES.
The EESV framework was designed to holistically streamline and standardize the collection of data on ecosystem services. It proposes to focus on six classes of data to collect for any ecosystem service of interest. The six EESV classes are: Ecological Supply, Use, Demand, Anthropogenic Contribution, Instrumental Value, and Relational Value.
Ecological Supply is the ecosystem structure and functions that influence an ecosystem’s potential to deliver ESs.
Use is the actualized appropriation of an ES by humans.
Demand is the implicit or explicit desire or need for an ES.
Anthropogenic Contribution is the investments humans make to increase Ecological Supply or Use, often through the process of co-production.
Instrumental Value is the benefit(s) obtained from an ES.
Relational Value is the importance ascribed by people to positive relationships between humans and nature and among humans via nature. Relational Value represents a step towards plural valuation, which seeks to include values held by diverse groups and their unique worldviews (Jacobs et al. 2020, IPBES 2022, Pascual et al. 2023, Flood et al. 2021).
The partnership represented by a BON should determine the choice of EESVs to monitor, types of decisions to support, and who will be involved in monitoring the ES. In a recent study, Schwantes et al. (In press) proposed that monitoring EESVs be organized in four steps: (1) define EESVs and acquire primary data, (2) transform data into EESVs, (3) analyze and interpret trends in EESVs for decision-making, and (4) use EESVs to assess the impact of policy or management interventions.
Schwantes et al. (In press) applied the EESV framework to three ecosystem services in British Columbia including salmon fisheries, orca whale watching, and freshwater regulation; they point out the challenges associated with gathering the different types of social, economic, and ecological data needed to fit models that allow assessments of change and reasons for trends in the focal ecosystem service.
Flood et al. (2021) showed the importance of peatlands in Ireland for local communities by assigning value to cultural ecosystem services. Two major questions should also motivate HJBL partners to assign value to cultural ecosystem services in the HJBL:
- How can a more expansive range of current and historic peatland values be represented to better account for local knowledge, experiences, and cultural contexts?
- How can these emerging cultural values be made more visible to harness societal shifts in how peatlands are used, viewed, and valued to foster more sustainable future trajectories?”
They finally suggest three recommendations for integrating temporal and local dimensions to valuing cultural ecosystem services: remembering, reimaging and restoring.
2.3. Monitoring workflows: linking observation and EBV to indicators
GEO BON’s framework for monitoring biodiversity change involves the production of EBVs/EESVs and indicators from observations about the state of biodiversity. The Figure below shows a typical workflow where data from different primary sources of observations (e.g. in situ, remote sensing, field observations by local communities) are combined with biodiversity models to produce layers of spatial and temporal variation in ecosystem extent and species distribution EBVs.
In some cases, one EBV can be an input to a model to produce another EBV (see chapter 5 below). This information is then integrated and summarised within geographic or governance reporting units ((1) and (2) in the figure) to calculate an indicator of biodiversity change, which can then be used, for instance, for reporting progress towards an GBF target. Note that this indicator can be processed within any spatial unit (e.g. a watershed, an ecoregion, to a country, or an entire biome).
EBVs and models can also be used to project changes in the indicator using scenarios (see chapter 6 below). Although both raw observations and indicators might change in the future, including with the development of new observation techniques and the expression of new user needs, the EBVs should, by definition, remain the same.
3. Arctic Observatories
The Sustaining Arctic Observing Networks (SAON)
The Sustaining Arctic Observing Networks (SAON) facilitates, coordinates, and advocates for coordinated international pan-Arctic observations and mobilizes the support needed to sustain them. The SAON “was initiated in 2011 to address the persistent shortcomings in the coordination of Arctic observations that are maintained by its many national and organizational partners. SAON set forth a bold vision in its 2018 strategic plan to develop a roadmap for Arctic observing and data systems (ROADS) to specifically address a key gap in coordination efforts—the current lack of a systematic planning mechanism to develop and link observing and data system requirements and implementation strategies in the Arctic region” (Starkweather et al. 2021).
SAON has included three main objectives in its Strategy 2018-2028:
Create a roadmap to a well-integrated Arctic Observing System;
Promote free and ethically open access to all Arctic observational data; and,
Ensure sustainability of Arctic observing.
The Shared Arctic Variable Framework main objective is to link local to global observing systems, by “identifying benefits to Indigenous communities, supporting fundamental understanding of Arctic systems and regional decision-making needs, and informing science and decision-making needs at the global scale and integrating with operational global networks” (Starkweather et al. 2021). Shared Arctic Variables (SAVs) are unique because they recognize 1) the role of Indigenous Peoples of the Arctic; 2) include multiple sectors and biophysical components; 3) limited resources means being strategic;and, 4) an unique environment (Bradley et al. 2022). HJBL is an opportunity to articulate global and regional initiatives along with community based monitoring that we describe below.
Canadian Consortium for Arctic Interoperability
The Canadian Consortium for Arctic Data Interoperability (CCADI) is an initiative to develop an integrated Canadian arctic data management system that will facilitate information discovery, establish sharing standards, enable interoperability among existing data infrastructures, and that will be co-designed with, and accessible to, a broad user base. Key to the CCADI vision are: standards and mechanisms for metadata, data and semantic interoperability; a distributed data exchange platform; streamlined data services with common entry, access, search, match, analysis, visualization and output tools; an intellectual property and sensitive data service; and data stewardship capacity.
A key intent of the GEO BON - Parks Canada - ECCC workshop held in Montreal, October 2023, was information gathering on data resources, technical capacities, and technological knowledge, systems, and methodologies to mobilize biodiversity and conservation knowledge to action. This workshop focused on investigations of the components for the formation of a potential ‘CanBON’, a Canadian Biodiversity Observation Network (BON), based on the conceptual framework provided by the GEO (Group on Earth Observations) flagship BON work programme. Working closely with the Cree communities in the Hudson and James Bay Lowlands, and the Mushkegowuk Council that represents them collectively, for example, a CanBON knowledge mobilization system would necessarily accommodate different knowledge systems, approaches to conservation and land stewardship, and cultural and data sensitivities.
This document describes the technical framework and distributed, interoperable architectural approach of the Canadian Consortium for Arctic Data Interoperability (CCADI) as one potential contribution to a CanBON. The CCADI framework developed from collaborations beginning in 2015 among Canadian Arctic data centres having a common goal of providing ethically open, accessible, and comprehensive digital resources to the broadest possible audience of data users. The initiative was developed to integrate the Canadian arctic data management system to establish sharing standards, enable interoperability among existing data infrastructures and diverse data streams, co-designed with, and accessible to, a broad user base. In 2018, CCADI received funding from the Canadian Foundation for Innovation (CFI) to develop an Arctic Research Data Infrastructure (ARDI). The ARDI is an evolving integrated Canadian Arctic data management system that facilitates information discovery, establishes sharing standards, enables interoperability among existing data infrastructures, and that is co-designed with, and accessible to, a broad user base. The base components remain in use today. Key to the CCADI vision and its base components include: standards and mechanisms for metadata, data, and semantic interoperability; a distributed data exchange platform; streamlined data services with common entry, access, search, match, analysis, visualization and output tools; an intellectual property and sensitive data service; and data stewardship capacity.
The CCADI technical framework builds on more than two decades of collaborative work in the arctic research data management community focused on harnessing the power of data and information technologies to support knowledge construction, information decision making and improving lives and livelihoods. This framework includes more than the software and technical systems, but also the diverse data streams, information products, language and worldviews, and social networks—all of which exist and evolve in a socio-technical ecosystem. The socio-technical ecosystem is a foundational concept in CCADI, and is described in greater detail in the bullet point below.
Based on a retrospective of GCRC’s experiences from the engagement in several international and national scale projects, this document briefly outlines a model of the technical and non-technical dimensions that have been central to achieving goals set out by diverse “communities” such as those represented in the CCADI consortium. We recognize the challenges in operationalizing western research for actionable, grounded outcomes, the limited social and technical connections that foster this action, and the uncertainties introduced by the rapidly changing broader data and information ecosystem (e.g., generative AI, cybersecurity threats). Thus, the CCADI framework is presented in the spirit of enhancing options that leverage opportunities and address challenges in the Canadian context broadly. This framework is also able to integrate new opportunities that are emerging such as increased recognition of Indigenous knowledge, data, and world views, generative AI and machine learning, semantic modeling, and new interdisciplinary data sources (such as community-based monitoring applications built on custom, open source, or proprietary software).
Key components of a CCADI model include:
- Socio-Technical Systems: A foundational concept in the CCADI initiative is seeing the overall system as a socio-technical system that includes various kinds of technology (that is not confined to information technology), technical processes, and social processes. Socio-technical refers to the interrelatedness of social and technical aspects of an organization or systems as a whole. A CCADI information ecology analytical tool package adopted a visual mapping approach to draw out the links between language, the natural environment, social and cultural practices, heritage, and customary practices and rituals, as appropriate to share with those outside of the community, and to meet the shared goals of the data ecosystem.
- Collaborative Approach: Integrating a wide range of actors in a data ecosystem through the CCADI framework necessarily takes a collaborative approach. The co-development mechanisms are reflexive, iterative, and adaptable to change if the desire to share and participate changes. The CCADI architecture includes a metadata and semantics mediator to enable interoperability over the spectrum of standards and variances in terminology or conceptualizations to share across different sciences or knowledge systems.
- Standards and Web Protocols: A CCADI-like system architecture would be based on international standards specifications (for data, metadata, and internet exchange protocols) such as those published by the Open Geospatial Consortium (OGC). Based on free and open-source software (FOSS) components, a modular or transportable framework that can be deployed many times, and hosted anywhere. This translates to technical and administrative practicality for partner collaborations to launch their own independent partner node in the system in a distributed approach. In this approach, partner nodes can, together, act as distributed nodal networks that use Application Programming Interface (API) exchange protocols to connect and become accessible.
- Principles: The diversity of partners in the CCADI system, all having potentially widely different data and information access, control, and sharing constraints and concerns requires guiding principles to enable these spheres to interact in a cohesive system that meets project goals and works to the benefit of all partners. CCADI architecture is built on a number of principles to include: FAIR principles (Findable, Accessible, Interoperable, and Reusable); IASC Statement of Principles and Practices for Arctic Data Management, which serves to bridging between Traditional Knowledge, Indigenous Knowledge, and “Western” scientific knowledge such that they are seen as equal and complementary; The Inuit Tapiriit Kanatami (ITK) National Inuit Strategy on Research (NISR); CARE Principles for Indigenous Data Governance—Collective Benefit, Authority to Control, Responsibility, and Ethics; and, The First Nations principles of ownership, control, access, and possession – more commonly known as OCAP.
4. Implementing a BON as Knowledge Hub
In practice, GEO BON suggests a stepwise, iterative approach to establishing and implementing BONs, drawing upon existing processes, standards, and tools and offering a multi-knowledge platform or hub . Implementing a knowledge hub involves four development phases: engagement, assessment, design, and implementation. This flexible approach has been used and adapted for the Arctic, Australia’s New South Wales, Colombia, Finland and most recently for EuropaBON.
The assessment phase of the BON development aims to capitalize on existing data and knowledge infrastructures. For instance, the French BON identified over 130 in situ observation infrastructures, mostly observing EBVs within the species traits, species populations, and genetic composition classes. Similarly, a Finnish assessment of the national indicators and the biodiversity monitoring programs underlying them showed that aside from species populations and ecosystem structure, most EBV classes are still poorly covered by the Finnish monitoring system. The same observation was made for the Colombia BON which identified over 100 different tools for biodiversity observation, data management and reporting. These assessments thus help governments and organizations to prioritize and strategically fill key gaps in their existing or developing observation systems.
5. BON-in-a-Box
Core to the establishment of a harmonized monitoring system is the need for the scientific community to share data, knowledge, and tools. The accessibility, interoperability, and reporting of biodiversity data and indicator workflows (e.g. code-based scripts for running models) is paramount. To support this effort, GEO BON has launched BON-in-a-Box to support organizations with their need to make knowledge guiding data and monitoring information available to all.
GEO BON’s large community of members contributes to the development of EBVs and the models used to explain biodiversity change and calculate indicators designed to inform policy action. BON-in-a-Box is an open-source platform designed to i) support the use of indicators by parties to the CBD, ii) supporting local communities with the use of their data to calculate indicators, and iii) working with businesses and financial institutions to guide capital flows to mitigate biodiversity change restore ecosystems.
BON-in-a-Box interlinks the available data, models, and computing and data science in a knowledge service making it available to all . It accelerates the development of predictive workflows and EBV-based indicator products that support biodiversity change detection and attribution across BONs varying geographic extents. BON-in-a-Box is ready to be applied to the HJBL.
Community-based monitoring and information systems (CBMIS) refer to initiatives by indigenous peoples and local community organisations to monitor their community’s well-being and the state of their territories and natural resources, applying a mix of traditional knowledge and innovative tools and approaches (Ferreira et al. 2015).
Cariño & Ferrari (2023) suggested some transitions based on the Forest People Programme, Local Biodiversity Outlook 2 (FPP 2020) to mainstream and prioritize support for the rights and collective actions of Indigenous Peoples and local communities (IPLC) throughout the post-2020 Global Biodiversity Framework.
- Land transitions: “The territories of life of IPLC, including their distinct cultural, spiritual and customary relationships with their lands and waters and their intrinsic and vital contributions to human wellbeing, biodiversity conservation and climate change mitigation and adaptation, are secured. The collective lands, territories and resources of IPLC are legally recognized and protected in keeping with international law; land-use classifications and land registration to uphold customary tenure are reformed; and the global coverage of areas conserved, sustainably used and restored are progressively and significantly increased” (FPP 2020).
- Economic transitions: “Diverse and human-scale economic systems are thriving, within which IPLC customary sustainable use and other small-scale producers are contributing to sustainable and resilient economies and scaled-down consumption patterns are guaranteeing a sustainable and just society” (FPP 2020).
- Food transitions: “Vibrant ecosystems and cultures ensure genetic diversity and diverse diets, improving health, resilience and livelihoods. Revitalized Indigenous and local food systems contribute to local food security, food sovereignty and agroecology, and underpin a just agricultural transition” (FPP 2020).
- Governance: “Vibrant ecosystems and cultures ensure genetic diversity and diverse diets, improving health, resilience and livelihoods. Revitalized Indigenous and local food systems contribute to local food security, food sovereignty and agroecology, and underpin a just agricultural transition”(FPP 2020).
- Incentives and financial transitions: “Incentives, including financial support for IPLC collective actions and innovative culture-based solutions, are prioritized; environmental, social, and human rights safeguards on biodiversity financing are applied; and perverse incentives and harmful investments are ended or redirected” (FPP 2020).